add batchnorm and batchnorm_test

src/operators/batchnorm_op.cpp

+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+==============================================================================*/
+
+#include "batchnorm_op.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <typename Dtype, typename T>
+void BatchNormOp<Dtype, T>::InferShape() const {
+    auto x_dims = param_.InputX()->dims();
+    param_.OutputY()->Resize(x_dims);
+}
+template class BatchNormOp<CPU, float>;
+} // namespace operators
+} // namespace paddle_mobile

src/operators/batchnorm_op.h

+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+==============================================================================*/
+
+#include "framework/operator.h"
+#include "operators/kernel/batchnorm_kernel.h"
+#include "operators/op_param.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+using namespace framework;
+
+template <typename DeviceType, typename T>
+class BatchNormOp : public framework::OperatorWithKernel<DeviceType> {
+  public:
+    BatchNormOp(const std::string &type, const VariableNameMap &inputs,
+                const VariableNameMap &outputs,
+                const framework::AttributeMap attrs,
+                std::shared_ptr<framework::Scope> scope)
+        : framework::OperatorWithKernel<DeviceType>(type, inputs, outputs,
+                                                    attrs, scope),
+          param_(inputs, outputs, attrs, *scope) {}
+
+    void Run() const {
+        operators::BatchNormKernel<DeviceType, T> kernel;
+        kernel.Compute(param_);
+    }
+
+    using framework::OperatorWithKernel<DeviceType>::OperatorWithKernel;
+    void InferShape() const override;
+
+  protected:
+    BatchNormParam param_;
+};
+
+} // namespace operators
+} // namespace paddle_mobile

src/operators/kernel/arm/batchnorm_kernel.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "operators/kernel/batchnorm_kernel.h"
+
+namespace paddle_mobile {
+namespace operators {
+
+template <>
+void BatchNormKernel<CPU, float>::Compute(const BatchNormParam &param) const {
+    /// todo: test.
+    const Tensor *input_x = param.InputX();
+    auto input_x_ptr = input_x->data<float>();
+    const auto &x_dims = input_x->dims();
+    const int N = x_dims[0];
+    const int C = x_dims[1];
+    const int H = x_dims[2];
+    const int W = x_dims[3];
+    const int stride0 = C * H * W;
+    const int stride1 = H * W;
+    const int stride2 = W;
+    Tensor *out = param.OutputY();
+    auto out_ptr = out->mutable_data<float>();
+    const float epsilon = param.Epsilon();
+    const Tensor *mean = param.InputMean();
+    const Tensor *variance = param.InputVariance();
+    const Tensor *scale = param.InputScale();
+    const Tensor *bias = param.InputBias();
+    auto mean_ptr = mean->data<float>();
+    auto variance_ptr = variance->data<float>();
+    auto scale_ptr = scale->data<float>();
+    auto bias_ptr = bias->data<float>();
+
+    Tensor inv_std;
+    auto inv_std_ptr = inv_std.mutable_data<float>(make_ddim({C}));
+    if (C != variance->numel()) {
+        std::cout << "C must equal to variance.numel()" << std::endl;
+    }
+    assert(C == variance->numel());
+
+    /// std = (var + epsilon).sqrt();
+    /// inv_std = 1 / std;
+    for (int i = 0; i < C; i++) {
+        inv_std_ptr[i] =
+            1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
+    }
+
+    Tensor new_scale;
+    auto new_scale_ptr = new_scale.mutable_data<float>(make_ddim({C}));
+    Tensor new_bias;
+    auto new_bias_ptr = new_bias.mutable_data<float>(make_ddim({C}));
+
+    /// ((x - est_mean) * (inv_var) * scale + bias equal to
+    /// (x * inv_var * scale) + (bias - est_mean * inv_var * scale)
+    for (int i = 0; i < C; i++) {
+        new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
+        new_bias_ptr[i] =
+            bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
+        {
+            for (int n = 0; n < N; n++) {
+                for (int h = 0; h < H; h++) {
+                    for (int w = 0; w < W; w++) {
+                        int index = n * stride0 + i * stride1 + h * stride2 + w;
+                        out_ptr[index] = input_x_ptr[index] * new_scale_ptr[i] +
+                                         new_bias_ptr[i];
+                    }
+                }
+            }
+        }
+    }
+    DLOG << "input[2,5,1,0](input[102]) ,channel 5 :";
+    DLOG << "input_x_ptr : " << input_x_ptr[102];
+    DLOG << "variance : " << variance_ptr[5];
+    DLOG << "inv_std_ptr : " << inv_std_ptr[5];
+    DLOG << "new_scale_ptr : " << new_scale_ptr[5];
+    DLOG << "new_bias_ptr : " << new_bias_ptr[5];
+    DLOG << "out_ptr : " << out_ptr[102];
+}
+} // namespace operators
+} // namespace paddle_mobile

src/operators/kernel/batchnorm_kernel.h

+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+==============================================================================*/
+
+#include "framework/operator.h"
+#include "operators/op_param.h"
+#pragma once;
+
+namespace paddle_mobile {
+namespace operators {
+
+using namespace framework;
+
+template <typename DeviceType, typename T>
+class BatchNormKernel
+    : public framework::OpKernelBase<DeviceType, BatchNormParam> {
+  public:
+    void Compute(const BatchNormParam &param) const;
+};
+
+} // namespace operators
+} // namespace paddle_mobile

src/operators/op_param.h

@@ -48,6 +48,25 @@ class OpParam : PaddleMobileObject {
         return GetVarValue<T>("Y", inputs, scope);
     }
 
+    template <typename T>
+    static T *InputBiasFrom(const VariableNameMap &inputs, const Scope &scope) {
+        return GetVarValue<T>("Bias", inputs, scope);
+    }
+    template <typename T>
+    static T *InputVarianceFrom(const VariableNameMap &inputs,
+                                const Scope &scope) {
+        return GetVarValue<T>("Variance", inputs, scope);
+    }
+    template <typename T>
+    static T *InputMeanFrom(const VariableNameMap &inputs, const Scope &scope) {
+        return GetVarValue<T>("Mean", inputs, scope);
+    }
+    template <typename T>
+    static T *InputScaleFrom(const VariableNameMap &inputs,
+                             const Scope &scope) {
+        return GetVarValue<T>("Scale", inputs, scope);
+    }
+
     template <typename T>
     static std::vector<T *> InputMultiFrom(const VariableNameMap &inputs,
                                            const Scope &scope) {
@@ -64,6 +83,11 @@ class OpParam : PaddleMobileObject {
         return GetVarValue<T>("Out", outputs, scope);
     }
 
+    template <typename T>
+    static T *OutputYFrom(const VariableNameMap &outputs, const Scope &scope) {
+        return GetVarValue<T>("Y", outputs, scope);
+    }
+
     template <typename T>
     static T *MidOutFrom(const VariableNameMap &outputs, const Scope &scope) {
         return GetVarValue<T>("MidOut", outputs, scope);
@@ -268,6 +292,54 @@ class LrnParam : public OpParam {
     float k_;
     std::string data_format_;
 };
+class BatchNormParam : OpParam {
+  public:
+    BatchNormParam(const VariableNameMap &inputs,
+                   const VariableNameMap &outputs,
+                   const framework::AttributeMap &attrs,
+                   const framework::Scope &scope) {
+        input_x_ = InputXFrom<framework::Tensor>(inputs, scope);
+        output_y_ = OutputYFrom<framework::Tensor>(outputs, scope);
+        input_bias_ = InputBiasFrom<framework::Tensor>(inputs, scope);
+        input_mean_ = InputMeanFrom<framework::Tensor>(inputs, scope);
+        input_scale_ = InputScaleFrom<framework::Tensor>(inputs, scope);
+        input_variance_ = InputVarianceFrom<framework::Tensor>(inputs, scope);
+        epsilon_ = GetAttr<float>("epsilon", attrs);
+        momentum_ = GetAttr<float>("momentum", attrs);
+        is_test_ = GetAttr<bool>("is_test", attrs);
+    }
+
+    const Tensor *InputX() const { return input_x_; }
+
+    Tensor *OutputY() const { return output_y_; }
+
+    const Tensor *InputBias() const { return input_bias_; }
+
+    const Tensor *InputMean() const { return input_mean_; }
+
+    const Tensor *InputScale() const { return input_scale_; }
+
+    const Tensor *InputVariance() const { return input_variance_; }
 
+    const float &Epsilon() const { return epsilon_; }
+
+    const float &Momentum() const { return momentum_; }
+
+    const bool &IsTest() const { return is_test_; }
+
+    const std::string &DataFormat() const { return data_format_; }
+
+  private:
+    Tensor *input_x_;
+    Tensor *output_y_;
+    Tensor *input_bias_;
+    Tensor *input_mean_;
+    Tensor *input_scale_;
+    Tensor *input_variance_;
+    float epsilon_;
+    float momentum_;
+    bool is_test_;
+    std::string data_format_;
+};
 } // namespace operators
 } // namespace paddle_mobile

test/CMakeLists.txt

@@ -19,6 +19,10 @@ target_link_libraries(test-concat-op paddle-mobile)
 ADD_EXECUTABLE(test-lrn-op  operators/test_lrn_op.cpp test_helper.h  test_include.h)
 target_link_libraries(test-lrn-op paddle-mobile)
 
+# gen test
+ADD_EXECUTABLE(test-batchnorm-op  operators/test_batchnorm_op.cpp test_helper.h  test_include.h)
+target_link_libraries(test-batchnorm-op paddle-mobile)
+
 # gen test log
 ADD_EXECUTABLE(test-log common/test_log.cpp)
 target_link_libraries(test-log paddle-mobile)

test/operators/test_batchnorm_op.cpp

+
+/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+==============================================================================*/
+#pragma once
+#include "../test_include.h"
+#include "operators/batchnorm_op.h"
+
+namespace paddle_mobile {
+namespace framework {
+
+template <typename Dtype> class TestBatchNormOp {
+  public:
+    explicit TestBatchNormOp(const Program<Dtype> p) : program_(p) {
+        if (use_optimize_) {
+            to_predict_program_ = program_.optimizeProgram;
+        } else {
+            to_predict_program_ = program_.originProgram;
+        }
+
+        const std::vector<std::shared_ptr<BlockDesc>> blocks =
+            to_predict_program_->Blocks();
+        //  DLOG << " **block size " << blocks.size();
+        for (int i = 0; i < blocks.size(); ++i) {
+            std::shared_ptr<BlockDesc> block_desc = blocks[i];
+            std::vector<std::shared_ptr<OpDesc>> ops = block_desc->Ops();
+            //    DLOG << " ops " << ops.size();
+            for (int j = 0; j < ops.size(); ++j) {
+                std::shared_ptr<OpDesc> op = ops[j];
+                if (op->Type() == "batch_norm" &&
+                    op->Input("X")[0] == "conv2d_0.tmp_0") {
+                    DLOG << " mul attr size: " << op->GetAttrMap().size();
+                    DLOG << " inputs size: " << op->GetInputs().size();
+                    DLOG << " outputs size: " << op->GetOutputs().size();
+                    DLOG << " Input X is : " << op->Input("X")[0];
+                    DLOG << " Input Mean is : " << op->Input("Mean")[0];
+                    DLOG << " Input Variance is : " << op->Input("Variance")[0];
+                    DLOG << " Input Scale is : " << op->Input("Scale")[0];
+                    DLOG << " Input Bias is : " << op->Input("Bias")[0];
+                    DLOG << " Output Y is : " << op->Output("Y")[0];
+                    DLOG << " epsilon : "
+                         << op->GetAttrMap().at("epsilon").Get<float>();
+                    std::shared_ptr<operators::BatchNormOp<Dtype, float>> lrn =
+                        std::make_shared<operators::BatchNormOp<Dtype, float>>(
+                            op->Type(), op->GetInputs(), op->GetOutputs(),
+                            op->GetAttrMap(), program_.scope);
+                    ops_of_block_[*block_desc.get()].push_back(lrn);
+                }
+            }
+        }
+    }
+
+    std::shared_ptr<Tensor> predict_bn(Tensor &t1, Tensor &t2, Tensor &t3,
+                                       Tensor &t4, Tensor &t5) {
+        // feed
+        auto scope = program_.scope;
+        Variable *x1_feed_value = scope->Var("conv2d_0.tmp_0");
+        auto tensor_x1 = x1_feed_value->GetMutable<Tensor>();
+        tensor_x1->ShareDataWith(t1);
+
+        Variable *mean_feed_value = scope->Var("batch_norm_0.w_1");
+        auto tensor_mean = mean_feed_value->GetMutable<Tensor>();
+        tensor_mean->ShareDataWith(t2);
+
+        Variable *scale_feed_value = scope->Var("batch_norm_0.w_0");
+        auto tensor_scale = scale_feed_value->GetMutable<Tensor>();
+        tensor_scale->ShareDataWith(t3);
+
+        Variable *variance_feed_value = scope->Var("batch_norm_0.w_2");
+        auto tensor_variance = variance_feed_value->GetMutable<Tensor>();
+        tensor_variance->ShareDataWith(t4);
+
+        Variable *bias_feed_value = scope->Var("batch_norm_0.b_0");
+        auto tensor_bias = bias_feed_value->GetMutable<Tensor>();
+        tensor_bias->ShareDataWith(t5);
+
+        Variable *output = scope->Var("batch_norm_0.tmp_2");
+        auto *output_tensor = output->GetMutable<Tensor>();
+        output_tensor->mutable_data<float>({4, 10, 2, 2});
+        //  DLOG << typeid(output_tensor).name();
+        //  DLOG << "output_tensor dims: " << output_tensor->dims();
+
+        std::shared_ptr<Tensor> out_tensor = std::make_shared<LoDTensor>();
+        out_tensor.reset(output_tensor);
+
+        predict_bn(t1, t2, t3, t4, t5, 0);
+        return out_tensor;
+    }
+
+  private:
+    const framework::Program<Dtype> program_;
+    std::shared_ptr<ProgramDesc> to_predict_program_;
+    std::map<framework::BlockDesc,
+             std::vector<std::shared_ptr<OperatorBase<Dtype>>>>
+        ops_of_block_;
+    bool use_optimize_ = false;
+
+    void predict_bn(const Tensor &t1, const Tensor &t2, const Tensor &t3,
+                    const Tensor &t4, const Tensor &t5, int block_id) {
+        std::shared_ptr<BlockDesc> to_predict_block =
+            to_predict_program_->Block(block_id);
+        for (int j = 0; j < ops_of_block_[*to_predict_block.get()].size();
+             ++j) {
+            auto op = ops_of_block_[*to_predict_block.get()][j];
+            DLOG << "op -> run()";
+            op->Run();
+        }
+    }
+};
+
+template class TestBatchNormOp<CPU>;
+} // namespace framework
+} // namespace paddle_mobile
+
+int main() {
+    DLOG << "----------**********----------";
+    DLOG << "begin to run BatchNormOp Test";
+    paddle_mobile::Loader<paddle_mobile::CPU> loader;
+    auto program = loader.Load(std::string(
+        "../../test/models/image_classification_resnet.inference.model"));
+
+    /// input x (4,10,2,2)
+    paddle_mobile::framework::Tensor inputx1;
+    SetupTensor<float>(&inputx1, {4, 10, 2, 2}, static_cast<float>(0),
+                       static_cast<float>(1));
+    auto *inputx1_ptr = inputx1.data<float>();
+
+    paddle_mobile::framework::Tensor mean;
+    SetupTensor<float>(&mean, {10}, static_cast<float>(0),
+                       static_cast<float>(1));
+    auto *mean_ptr = mean.data<float>();
+
+    paddle_mobile::framework::Tensor scale;
+    SetupTensor<float>(&scale, {10}, static_cast<float>(0),
+                       static_cast<float>(1));
+    auto *scale_ptr = scale.data<float>();
+
+    paddle_mobile::framework::Tensor variance;
+    SetupTensor<float>(&variance, {10}, static_cast<float>(0),
+                       static_cast<float>(1));
+    auto *variance_ptr = variance.data<float>();
+
+    paddle_mobile::framework::Tensor bias;
+    SetupTensor<float>(&bias, {10}, static_cast<float>(0),
+                       static_cast<float>(1));
+    auto *bias_ptr = bias.data<float>();
+
+    paddle_mobile::framework::TestBatchNormOp<paddle_mobile::CPU>
+        testBatchNormOp(program);
+
+    auto output_bn =
+        testBatchNormOp.predict_bn(inputx1, mean, scale, variance, bias);
+    auto *output_bn_ptr = output_bn->data<float>();
+
+    /// [2, 5, 1, 0]
+    DLOG << " (" << inputx1_ptr[102] << " - " << mean_ptr[5] << ")/(("
+         << variance_ptr[5] << " + 0.00001"
+         << ")^0.5)* " << scale_ptr[5] << " + " << bias_ptr[5] << " = ";
+    DLOG << output_bn_ptr[102];
+
+    return 0;
+}

test/operators/test_concat_op.cpp

@@ -41,21 +41,6 @@ template <typename Dtype> class TestConcatOp {
             //    DLOG << " ops " << ops.size();
             for (int j = 0; j < ops.size(); ++j) {
                 std::shared_ptr<OpDesc> op = ops[j];
-                //                        if (op->Type() == "mul") {
-                //                            DLOG << "x_num_col_dims :
-                //                            "
-                //                                 << op->GetAttrMap()
-                //                                        .at("x_num_col_dims")
-                //                                        .Get<int>();
-                //                            DLOG << "y_num_col_dims :
-                //                            "
-                //                                 << op->GetAttrMap()
-                //                                        .at("y_num_col_dims")
-                //                                        .Get<int>();
-                //                            DLOG << " Input X is : "
-                //                            << op->Input("X")[0];
-                //                        }
-                //                        DLOG << "op:" << op->Type();
                 if (op->Type() == "concat" &&
                     op->Input("X")[0] == "conv2d_3.tmp_1") {
                     DLOG << " mul attr size: " << op->GetAttrMap().size();

test/operators/test_elementwise_add_op.cpp

@@ -41,18 +41,6 @@ template <typename Dtype> class TestElementwiseAddOp {
             //    DLOG << " ops " << ops.size();
             for (int j = 0; j < ops.size(); ++j) {
                 std::shared_ptr<OpDesc> op = ops[j];
-                //                        if (op->Type() ==
-                //                        "elementwise_add") {
-                //                            if
-                //                            (op->GetAttrMap().at("axis").Get<int>()
-                //                            != -1) {
-                //                                DLOG << "attr: axis =
-                //                                "
-                //                                     <<
-                //                                     op->GetAttrMap().at("axis").Get<int>();
-                //                            }
-                //                        }
-                //                        DLOG << "op:" << op->Type();
                 if (op->Type() == "elementwise_add" &&
                     op->Input("X")[0] == "batch_norm_2.tmp_2") {
                     DLOG << " elementwise_add attr size: "

test/operators/test_lrn_op.cpp

@@ -41,21 +41,6 @@ template <typename Dtype> class TestLrnOp {
             //    DLOG << " ops " << ops.size();
             for (int j = 0; j < ops.size(); ++j) {
                 std::shared_ptr<OpDesc> op = ops[j];
-                //                        if (op->Type() == "mul") {
-                //                            DLOG << "x_num_col_dims :
-                //                            "
-                //                                 << op->GetAttrMap()
-                //                                        .at("x_num_col_dims")
-                //                                        .Get<int>();
-                //                            DLOG << "y_num_col_dims :
-                //                            "
-                //                                 << op->GetAttrMap()
-                //                                        .at("y_num_col_dims")
-                //                                        .Get<int>();
-                //                            DLOG << " Input X is : "
-                //                            << op->Input("X")[0];
-                //                        }
-                //                        DLOG << "op:" << op->Type();
                 if (op->Type() == "lrn" &&
                     op->Input("X")[0] == "pool2d_0.tmp_0") {
                     DLOG << " mul attr size: " << op->GetAttrMap().size();

test/operators/test_mul_op.cpp

@@ -41,21 +41,6 @@ template <typename Dtype> class TestMulOp {
             //    DLOG << " ops " << ops.size();
             for (int j = 0; j < ops.size(); ++j) {
                 std::shared_ptr<OpDesc> op = ops[j];
-                //                        if (op->Type() == "mul") {
-                //                            DLOG << "x_num_col_dims :
-                //                            "
-                //                                 << op->GetAttrMap()
-                //                                        .at("x_num_col_dims")
-                //                                        .Get<int>();
-                //                            DLOG << "y_num_col_dims :
-                //                            "
-                //                                 << op->GetAttrMap()
-                //                                        .at("y_num_col_dims")
-                //                                        .Get<int>();
-                //                            DLOG << " Input X is : "
-                //                            << op->Input("X")[0];
-                //                        }
-                //                        DLOG << "op:" << op->Type();
                 if (op->Type() == "mul" &&
                     op->Input("X")[0] == "pool2d_0.tmp_0") {
                     DLOG << " mul attr size: " << op->GetAttrMap().size();